The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

Bach, Małgorzata; Werner, A.; Żywiec, Joanna; Pluskiewicz, Wojciech

doi:10.1016/j.ins.2016.09.038

Cited by 107 publications

(53 citation statements)

References 44 publications

(58 reference statements)

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“…Bach et al, in 2016 [16], analyzed a dataset of 729 patients. In total, 92.6% belonged to healthy cases and 7% of cases suffered from Osteoporosis.…”

Section: Related Workmentioning

confidence: 99%

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

et al. 2020

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Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data.

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“…Bach et al, in 2016 [16], analyzed a dataset of 729 patients. In total, 92.6% belonged to healthy cases and 7% of cases suffered from Osteoporosis.…”

Section: Related Workmentioning

confidence: 99%

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

et al. 2020

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“…It clusters individual classes beforehand to consequently process under-over sampling [37,38]. The clue is to discover and reduce the pure clusters of the highest majority and each minority classes.…”

Section: Ucpmot: a Rationalized Techniquementioning

confidence: 99%

“…They enclose an extreme number of instances (38,50,505) and attributes (4932) with wide-ranging I.R. (623).…”

Section: Ormentioning

confidence: 99%

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Patil¹,

Sonavane²

2017

J Big Data

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“…Different approaches have been proposed to solve this problem, trying to minimize the imbalanced ratio such as works in [5]- [10]. Such approaches could be classified as under-sampling and oversampling approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Such approaches could be classified as under-sampling and oversampling approaches. In the under-sampling approaches such as works in [1], and [10] some of the majority class instances are randomly deleted to Islam Elgedawy is with Computer Engineering Department, Middle East Technical University, Northern Cyprus Campus, 99738, Kalkanlı, Guzelyurt, Mersin 10, Turkey ( e-mail: elgedawy@metu.edu.tr).…”

Section: Introductionmentioning

confidence: 99%

AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

Majzoub¹,

Elgedawy²

2020

IJMLC

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SMOTE is an oversampling approach previously proposed to solve the imbalanced data binary classification problem. SMOTE managed to improve the classification accuracy, however it needs to generate large number of synthetic instances, which is not efficient in terms of memory and time. To overcome such drawbacks, the Borderline-SMOTE (BSMOTE) is previously proposed to minimize the number of generated synthetic instances by generating such instances based on the borderline between the majority and minority classes. Unfortunately, BSMOTE could not provide big savings regarding the number of generated instances, trading to the classification accuracy. To improve BSMOTE accuracy, this paper proposes an Affinitive Borderline SMOTE (AB-SMOTE) that leverages the BSMOTE, and improves the quality of the generated synthetic data by taking into consideration the affinity of the borderline instances. Experiments' results show the AB-SOMTE, when compared with BSMOTE, managed to produce the most accurate results in the majority of the test cases adopted in our study.

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The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

Cited by 107 publications

References 44 publications

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

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